An application of EDA and GA to dynamic pricing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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We extend Lemke's algorithm to solve a dynamic pricing problem. We identify an instance in which Lemke's algorithm fails to converge to an optimal solution (when an optimum does exist) and propose a constraint logic programming solution to this problem. We analyze the complexity of the extended Lemke's algorithm. Our analysis shows that, in the short term, dynamic pricing can be used to improve resource management efficiency. It is also shown that dynamic pricing can be used to manage the long-term behavior of demand.